HCI - Reading Notes

Week 1 - Foundations of HCI

MacKenzie, I.S. (2013). Chapter 1: Historical Context. Human-Computer Interaction: An Empirical Research Perspective.

  • predecessor to HCI is human factors or ergonomics
  • concerned with human capabilities, limitation, performance, and designs that fit within these params
  • HCI is narrowing this definition to human interaction with computing technology

Historical Context

  • "As we may think", Vannevar Bush - memex ("associative thinking"), connect points of interest. ie. hyperlink/bookmarks.
  • Ivan Sutherland's Sketchpad - manipulation of geometric shapes and lines (objects) on a display using a light pen. Significance: direct manipulation of the interface.
  • Invention of the mouse (1964) - Invented by Douglas Engelbart. Direct manipulation on screen. Require "on-screen tracker" to establish correspondence between device space and display space.
    • Other devices, joystick, lightpen, knee-controlled lever, "grafacon". Evaluation, mouse is most accurate, knee-lever is fastest.
  • Xerox star (1981) - first commercially released computer system with a GUI. It had windows, icons, menus, and a pointing device (WIMP). It supported direct manipulation and what-you-see-is-what-you-get (WYSIWYG) interaction.
    • Breaks from CLIs, which use a sequential programming paradigm.
    • Direct manipulation requires different approach. Uses event-driven programming.
  • Birth of HCI (1983) - Three key events as markers: the first ACM SIGCHI conference, the publication of Card, Moran, and Newell’s The Psychology of Human-Computer Interaction (1983), and the arrival of the Apple Macintosh, pre-announced with flyers in December 1983
    • ACM SIGCHI conference - association of professionals who work in the research and practice of computer-human interaction
    • "Psychology of human-computer interaction", Card, Moran, Newel - Concepts, human perceptual input (e.g., the time to visually perceive a stimulus), cognition (e.g., the time to decide on the appropriate reaction), and motor output (e.g., the time to react and move the hand or cursor to a target).
      • Significance: Theory for designers of interfaces. "Convincingly demonstrates why and how models are important and to teach us how to build them."
      • Context is the milieu of basic research in human-computer interaction and related fields.
      • "Whether generating quanti- tative predictions across alternative design choices or delimiting a problem space to reveal new relationships, a model’s purpose is to tease out strengths and weak- nesses in a hypothetical design and to elicit opportunities to improve the design."
    • Apple Macintosh - like Xerox Star, but catered to masses.

Growth of GUIs

Growth of HCI Research

Early topics:

  • Quality, effectiveness, and efficiency of the interface. How quickly and accurately can people do common tasks using a GUI versus a text-based command-line interface?
  • Menu design - recognition (selecting a command) vs recall (typing), depth vs breadth.

Norman, D. (2013). Chapter 1: The Psychopathology of Everyday Things

Two of the most important characteristics of good design are discoverability and understanding

  1. Discoverability: Is it possible to even figure out what actions are possible and where and how to perform them?
  2. Understanding: What does it all mean? How is the product supposed to be used? What do all the different controls and settings mean?

Fields of design:

  1. Industrial design: The professional service of creating and developing concepts and specifications that optimize the function, value, and appearance of products and systems for the mutual benefit of both user and manufacturer (from the Industrial Design Society of America’s website).
  2. Interaction design: The focus is upon how people interact with technology. The goal is to enhance people’s understanding of what can be done, what is happening, and what has just occurred. Interaction design draws upon principles of psychology, design, art, and emotion to ensure a positive, enjoyable experience.
  3. Experience design: The practice of designing products, processes, services, events, and environments with a focus placed on the quality and enjoyment of the total experience.

"We must design our machines on the assumption that people will make errors."

The role of HCD and Design Specializations

  • Starting with a good understanding of people and the needs that the design is intended to meet.
  • Getting the specification of the thing to be defined is one of the most difficult parts of the design, so much so that the HCD principle is to avoid specifying the problem as long as possible but instead to iterate upon repeated approximations.

Fundamental Principles of Interaction

  • Discoverability results from appropriate application of five fundamental psychological concepts covered in the next few chapters: affordances, signifiers, constraints, mappings, and feedback.
  • But there is a sixth principle: the conceptual model of the system. It is the conceptual model that provides true understanding

Affordance

  • The term affordance refers to the relationship between a physical object and a person.
  • An affordance is a relationship between the properties of an object and the capabilities of the agent that determine just how the object could possibly be used. E.g. chair affords ("is for") support, and therefore, affords sitting.
  • Affordances exist even if they are not visible. For designers, their visibility is critical: visible affordances provide strong clues to the operations of things

Signifiers

  • Signifiers: If an affordance or anti-affordance cannot be perceived, some means of signaling its presence is required
  • Signifier refers to any mark or sound, any perceivable indicator that communicates appropriate behavior to a person.
  • Clarification: A sign is NOT an affordance, it is a signifier.
  • Can be delibarte ("PUSH" sign) or unintentional (newly created path by humans).

Affordance vs Signifiers:

  • Affordances are the possible interactions between people and the environment. Some affordances are perceivable, others are not.
  • Perceived affordances often act as signifiers, but they can be ambiguous.
  • Signifiers signal things, in particular what actions are possible and how they should be done. Signifiers must be perceivable, else they fail to function.

Mapping: When the mapping uses spatial correspondence between the layout of the controls and the devices being controlled, it is easy to determine how to use them

Feedback

  • Requirements: immediate, informative, just the right amount.

Conceptual Models

  • A conceptual model is an explanation, usually highly simplified, of how something works.
  • It doesn’t have to be complete or even accurate as long as it is useful.
  • Can be explained to user, or learned by experience.
  • Bad design: When controls suggest a false conceptual model (e.g. refrigerator with 2 controls for freezer / fridge, but are both affected by either control).

System Image

Definition: The system image is what can be derived from the physical structure that has been built (including documentation).

Norman, D. A. (1986). Cognitive engineering.

Goals:

  1. To understand the fundamental principles behind human action and performance that are relevant for the development of engineering principles of design.
  2. To devise systems that are pleasant to use - the goal is neither efficiency nor ease nor power, although these are all to be desired, but rather systems that are pleasant, even fun

Psychological Variables Differ From Physical Variables: In many situations, the variables easily controlled are not those that the user cares about.

  1. Mapping problems: Which control controls what?
  2. Ease of control
  3. Evaluation - determine if correct outcome has been reached.

Gulf of Execution / Evaluation

Week 2 - Research Ethics and Needfinding

MacKenzie, I.S. (2013). Chapter 4: Scientific Foundations. Human-Computer Interaction: An Empirical Research Perspective.

MacKenzie, I.S. (2013). Chapter 4: Scientific Foundations. Human-Computer Interaction: An Empirical Research Perspective. (pp. 121-152). Waltham, MA: Elsevier.

What is research?

3 definitions:

  1. Careful or diligent search - "Search" is key term, trying to find things.
  2. Collecting information about a particular subject - data gathering of a phenomenon.
  3. Research is investigation or experimentation aimed at the discovery and interpretation of facts and revision of accepted theories or laws in light of new facts.
    • In HCI, Experiment = user study
    • Empirical research - encompasses both experimental and non-experiment methods (e.g. buildilng interaction models)
    • Facts - what we seek in experimental research.
    • Theory - hypothesis of a phenomenon
    • Law - More constraining, accepted. e.g. Fitts' law of human motor behavior in HCI domain.

Additional characteristics of research:

  1. Research must be published - Why? Must extend, refine or revise the existsing body of knowledge in the field.
  2. Citations, references, impact - Connects ideas to other ideas. Suports intellectual honesty. Back up assertions.
    • Number of citations to paper = impact (e.g. H-index - quantifies both research prductivity and overall impact of a body of work)
  3. Reproducibility - Research that cannot be replicated is useless

Research versus engineering versus design

  • Engineers and designers are in the business of building things
    • Trade-off: form (design emphasis) and function (engineering emphasis)
  • Research: Narrow focus, small ideas conceived, prototyped, tested, advanced or discarded.
    • research prototype = mockups, not actual products.
    • "Prototypes should command only as much time, effort, and investment as are needed to generate useful feedback and evolve an idea."
    • "Researchers provide the raw materials and processes engineers and designers work with"

What is empirical resaerch?

Definitions:

  1. Originating in or based on observation or experience.
  2. Relying on experience or observation alone, often without due regard for system and theory
  3. Capable of being verified or disproved by observation or experiment

Research methods

Observational
  • What: interviews, field investigations, contextual inquiries, case studies, field studies, focus groups,
  • More qualitative
  • Achieves relevance while sacrificing precision - Real world phenomena are high in relevance, but lack the precision available in controlled laboratory experiments.
  • Focus on why and how
Experimental
  • What: Controlled experiments. Include manipulated variable and response variable (independent vs dependent)
  • Comparison of manipulated variables is key, otherwise not experimental research.
  • The relationship between the independent variable and the dependent variable is one of cause and effect
Correlational
  • What: Look for relationships between variables (e.g. age, income, gender)
  • How: Observation, interviews, surveys, etc.
  • Correlational methods provide a balance between relevance and precision

Observe and measure

How are observations made:

  1. Another human as observer - manual entry
  2. An apparatus is the observer - automatic logs by a computer

Measurement scales:

  1. Nominal - arbitrarily assigning a code to an attribute or a category (license plate numbers, zip codes)
    • Used to count frequency
  2. Ordinal - provide an order or ranking to an attribute
    • Implies ranking
    • Comparison of greater than or less than are possible.
    • Not valid to compute the mean of ordinal data.
  3. Interval - equal distances between adjacent values, but no absolute zero (e.g. temperature Fahrenheit or Celsius)
    • used in questionnaires where a response on a linear scale is solicited (e.g. Likert scale)
  4. Ratio - Ratio data have an absolute zero and support a myriad of calculations to summarize, compare, and test the data.
    • Mathetmatical operations and stats are possible (add/subtract/mean/stdev)
    • Example: Time, occurrence counts
    • normaliation: standardizes and makes easier for comparison (e.g. words-per-minute, error-rate)

Research questions

What: Conduct experimental research to answer (and raise) questions about a new or existing user interface or interaction technique.

Difficulty: People exhibit variable behavior, which affects confidence in our findings.

Questions:

  • Is the new technique any good?
  • Is the new technique better than (interface)?
  • Is the new technique faster than (interface)?
  • Is the new technique faster than (inteface) after a bit of practice?
  • Is the measured entry speed (in words per minute) higher for the new technique than for a (interface) after one hour of use?
Internal validity and external validity

Definition: Accuracy of answer (internal) vs breadth of question (external)

  1. Internal validity (definition) is the extent to which an effect observed is due to the test conditions.
    • Why? We want confidence that the difference observed was actually due to inherent differences between the tech- niques.
  2. External validity (definition) is the extent to which experimental results are generalizable to other people and other situations.
    • Why? To the extent the research pursues broadly framed questions, the results tend to be broadly applicable.

Tradeoffs:

  • Effort to improve external validity through environmental considerations may negatively impact internal validity.
  • The desire to improve external validity through procedural considerations may nega- tively impact internal validity.

Ecological validity vs external validity:

  • Ecological = Methodology (using materials, tasks, and situations typical of the real world)
  • External = Outcome (obtaining results that generalize to a broad range of people and situations).

Comparative evaluations

Takeaway: "A comparative evaluation yields more valuable and insightful results than a single-interface evaluation"

Relationships: circumstantial and causal

Causal relationship: "condition manipulated in the experiment caused the changes in the human responses that were observed and measured"

  • Different from circumstantial (e.g. cigarettes and cancer)
  • Examined by controlled experiments, where only one variable is changed.
  • Caveat: If the variable manipulated is a naturally occurring attribute of participants, then cause and effect conclusions are unreliable.
    • e.g. gender (female, male), person- ality (extrovert, introvert), handedness (left, right)

Research topics

Finding a topic:

  1. Think small - Narrow down the problem to sub-problems.
  2. Replicate - Replicate an existing experiment from literature. This is an empowering process.
  3. Know the literature
  4. Think inside the box - Just get on with your day, but at every juncture, every interaction, think and question. What happened? Why did it happen? Is there an alternative?

Müller, H., Sedley, A., & Ferrall-Nunge, E. (2014). Survey research in HCI

Müller, H., Sedley, A., & Ferrall-Nunge, E. (2014). Survey research in HCI. In J. Olson & W. Kellogg (Eds.) Ways of Knowing in HCI (pp. 229-266). New York: Springer.

What Questions the Method Can Answer

  1. Measure attitudes
  2. Measure intent
  3. Quantify task success
  4. UX feedback
  5. User characteristics - understand a system's users
  6. Interactions with technology - how users interact with technology in broad terms (social, demographic)
  7. Awareness - helps understand people's awareness of existing technologies
  8. Comparisons - compare users' attitudes / perceptions / experiences across segments, time, geographies, etc.

When to avoid surveys

  1. Precise behaviors - gather from log data instead
  2. Underlying motiviations - users often don't know motivation. Use ethnography or contextual inquiry instead.
  3. Usability evaluations - why users succeeded / failed in a task. Use interviews instead

How to Survey

Research goals and constructs
  • Do the survey constructs focus on results which will directly address research goals and inform stakeholders’ decision making rather than providing merely informative data?
  • Will the results be used for longitudinal comparisons or for one-time decisions?
  • What is the number of responses needed to provide the appropriate level of precision for the insights needed?
Population and sampling
  • Random sampling is best, minimizes sampling bias. e.g. random phone number, address-based surveys.
  • Non-probability sampling - snowball recruiting, convenience samples (target ppl easily available). Higher potential for bias.
  • Choosing sample size - determine margin of error. Commonly used are 3-5%. Confidence level indicates how likely the reported metric falls within the margin of error. Typically 95%.
Questionnaire design and biases

Common biases: Satisficing - Respondents use suboptimal amount of effort.

- Respondents are more likely to satisfice when (Krosnick, 1991):
    - Cognitive ability to answer is low.
    - Motivation to answer is low.
    - Question difficulty is high at one of the four stages, resulting in cognitive exertion.
- Avoid by:
    - Keeping answers concise
    - Avoid using same rating scale in series
    - Avoid long surveys
    - Explain importance of survey
    - Avoid trap questions (e.g. "enter 5 in the following box")

Acquiescence Bias - Respondents want to please the surveyer.

- Avoid by:
    1. Using agree/disagree, yes/no, true/false answers
    2. Ask Qs about the underlying construct (?)
    3. Use reverse-keyed constructs (asking same construct both positive and negative).

Social Desirability - respondents answer questions in a manner they feel will be positively perceived by others

- Avoid by allowing anonymous answers.

Response Orer Bias - tendency to select the items toward the beginning or the end of an answer or scale.

Question Order Bias - Each question in a survey has the potential to bias each subsequent question by priming respondents

Review and survey pretesting

Cognitive Pretesting - take the survey while using the think-aloud protocol (similar to a usability study).

Field Testing - Piloting the survey with a small subset of the sample

Implementation and launch

Monitoring Survey Paradata

  • Click-through rate: Of those invited, how many opened the survey.
  • Completion rate: Of those who opened the survey, how many finished the survey.
  • Response rate: Of those invited, how many finished the survey.
  • Break-off rate: Of those who started, how many dropped off on each page. • Completion time: The time it took respondents to finish the entire survey.

Maximizing response rates: "Total Design Method":

  1. Week 1: Initial request with survey
  2. Week 2: Reminder postcard
  3. Week 4: Replacement survey to non-respondents
  4. Week 7: Second replacement survey to non-respondents.

One strategy to maximize the benefit of incentives is to offer a small non-contingent award to all invitees, followed by a larger contingent award to initial non-respondents (Lavrakas, 2011).

Data analysis and reporting

Cleaning:

  1. Dedupe
  2. Remove "speeders"
  3. Remove "straight liners"
  4. Fix missing data

Assessment:

  1. Low inter-item reliability - Respondents that give inconsistent or unreliable responses may signify they were not paying attention to questions.
  2. Outliers - 2 to 3 standard deviations from the mean.
  3. Inadequate open-ended responses - often lead to low quality response.

Hypothesis testing - probability of a hypothesis being true when comparing groups (using t-test, ANOVA, Chi-square)

Inferential statistics can also be applied to identify connections among variables:

  1. Bivariate correlations are widely used to assess linear relationships between variables.
  2. Linear regression - proportion of variance in a continuous dependent variable.
  3. Logistic regression - predict change in probability of getting a particular value in a binary variable.
  4. Decision trees - probabilities of reaching specific outcomes
  5. Factor analysis - identify groups of covariates, reduce large number of variables into smaller set.
  6. Cluster analysis - categorizing segments.

Analysing Open-ended Responses:

  1. Coding - transform qualitative data to quantative
  2. Interrater reliability - Cohens Kappa

Week 3 - Invisible Interfaces and Human Abilities

Norman, D. (2013). Chapter 2: The Psychology of Everyday Actions

https://gatech.instructure.com/courses/340124/files/folder/Required%20Readings

Gulf of Evaluation reflects the amount of effort that the person must make to interpret the physical state of the device and to determine how well the expectations and intentions have been met

  • Mitigate with feedback and good conceptual model.

7 stages of action:

The specific actions bridge the gap between what we would like to have done (our goals) and all possible physical actions to achieve those goals.

  1. Goal (form the goal)
  2. Plan (the action)
  3. Specify (an action sequence) 4. Perform (the action sequence)
  4. Perceive (the state of the world)
  5. Interpret (the perception)
  6. Compare (the outcome with the goal)

Overlearning: Skills where performance is effortless, done automatically with little or no awareness.

Systems of Cognition

Subconscious and conscious systems of cognition (Table)

Human Cognition and Emotion

  1. Visceral level - "lizard brain". Basic protective mechanisms. Quick judgments. Completely subconscious.
    • In design: Immediate perception (ring tone, appearances).
  2. Behavioral level - learned skills, largely subconscious. E.g. motor skills.
    • In design: "For designers, the most critical aspect of the behavioral level is that every action is associated with an expectation."
  3. Reflective level - conscious cognition. Deep understanding.
    • In design: Reflection / looking back, evaluating, causality.
    • Memories are powerful tools in design. e.g. brand impact.

_"All three levels of processing work together to determine a person’s cognitive and emotional state. High-level reflective cognition can trigger lower-level emotions. Lower-level emotions can trigger higher-level reflective cognition." (Norman, p.55)

"Emotional Design" - design that uses all three.

Learned Helplessness

Definition: Situation where people experience repeated failure at a task. Thus, they decide the task can't be done and stop trying.

  • Ppl tend to blame themselves when tech doesn't work.
  • Math curriculum is like this. Each lessons assumes full knowledge of prior lesson.

Positive Psychology

  • Do not blame people when they fail to use your products properly.
    • e.g. "Didn't you read the manual?"
  • Take people’s difficulties as signifiers of where the product can be improved.
  • Eliminate all error messages from electronic or computer systems. Instead, provide help and guidance.
  • Make it possible to correct problems directly from help and guidance messages. Allow people to continue with their task: Don’t impede progress—help make it smooth and continuous. Never make people start over.
  • Assume that what people have done is partially correct, so if it is inappropriate, provide the guidance that allows them to correct the problem and be on their way.
  • Think positively, for yourself and for the people you interact with.

How Technology Can Accommodate Human Behavior

"When we collaborate with machines, it is people who must do all the accommodation. Why shouldn’t the machine be more friendly?"

"Many machines are programmed to be very fussy about the form of input they require, where the fussiness is not a requirement of the machine but due to the lack of consideration for people in the design of the software."

"Designers should strive to minimize the chance of inappropriate actions in the first place by using affordances, signifiers, good mapping, and constraints to guide the actions ... When people understand what has happened, what state the system is in, and what the most appropriate set of actions is, they can perform their activities more effectively."

The Seven Stages of Action: Seven Fundamental Design Principles

Seven stages of action

  1. What do I want to accomplish?
  2. What are the alternative action sequences?
  3. What action can I do now?
  4. How do I do it?
  5. What happened?
  6. What does it mean?
  7. Is this okay? Have I accomplished my goal?
  • Feedforward: Info that helps answer question of execution (doing)
  • Feedback: Info that aids in understanding what happened.

Seven fundamental principles of design

  1. Discoverability: Easy to determine what actions are possible
  2. Feedback: Information on whether the action they did anything, and what state it is now in.
  3. Conceptual model: The designer's intended mental model, or user's set of ideas about how the system is organized and operates.
  4. Affordances: The proper affordances exist to make the desired ac- tions possible.
  5. Signifiers: Effective use of signifiers ensures discoverability and that the feedback is well communicated and intelligible.
  6. Mappings: The relationship between controls and their actions fol- lows the principles of good mapping, enhanced as much as possible through spatial layout and temporal contiguity.
  7. Constraints. Providing physical, logical, semantic, and cultural constraints guides actions and eases interpretation.

MacKenzie, I.S. (2013). Chapter 2: The Human Factor. Human-Computer Interaction: An Empirical Research Perspective.

https://gatech.instructure.com/courses/340124/files/folder/Required%20Readings

Time scale of human action

What: Descriptive model of a human, different types of human actions in timneframes within which the actions occur.

Model's four bands:

  1. Biological band - quantitative, experimental
  2. Cognitive band -
  3. Rational band -
  4. Social band - qualitative, non-experimental
  • Each band is divided into three levels, 9 levels in total.

Human Factor

  • Dash is where the interaction takes place between human and machine.

Sensors

Vision

  • Light has intensity (brightness) and frequency (perception of color)
    • Fixations: eyes stationary, taking in environment
    • Sacades: Quick, 30-120ms.
  • Scanpath: Tracking eye movement. How users interpret an image / web page / etc. Has implications in advertising.

Hearing

Components:

  • Intensity: Sound pressure level. Painful at 120-140dB.
  • Frequency: Pitch. Humans perceive 20-20kHz
  • Timbre: Harmonic richness of sound.
  • Envelope: Change in amplitude over time.

Touch

Sensors: skin, muscles, bones, joints, and organs

  • Gets info about temperature, shape, texture, or position of the object, or the amount of resistance
  • Relelvant in augmenting UX through tactile feedback.

Smell and taste

  • Generally hard to incorporate into HCI. Brewster et al. (2006) studied smell as an aid in searching digital photo albums.

Responders

What: Motor control to affect the environment.

  • Movement of the limbs is tightly coupled to the somatosensory system
  • Proprioception: the coordination of limb movement and position through the perception of stimuli within muscles and tendons.)

Et cetera

(This chapter keeps going and going...)

Hutchins, E. L., Hollan, J. D., & Norman, D. A. (1985). Direct manipulation interfaces. Human–Computer Interaction

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.122.4927&rep=rep1&type=pdf

  • Covers distance and engagement
  • Two forms of distance: Semantic (how hard is it to know what do) and articulatory (how hard is it to execute it)

Week 4 - Design Alternatives

Faste, H., Rachmel, N., Essary, R., & Sheehan, E. (2013, April). Brainstorm, Chainstorm, Cheatstorm, Tweetstorm: new ideation strategies for distributed HCI design

Faste, H., Rachmel, N., Essary, R., & Sheehan, E. (2013, April). Brainstorm, Chainstorm, Cheatstorm, Tweetstorm: new ideation strategies for distributed HCI design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1343-1352). ACM.

http://henrybacondesign.com/wp-content/uploads/2017/02/Brainstorm_Chainstorm_Cheatstorm_Tweetst.pdf

Takeaways

  • "Chainstorming" - Variation of Brainstorm that emphasizes the connection between ideas. Participants build upon each other's suggestions, forming a chain of ideas. This approach helps in exploring interconnected concepts and encourages the development of more complex and innovative solutions.
  • "Cheatstorming" - Do bunch of brainstorm sessions, save ideas. Now next brainstorm session, people vote on existing brainstormed ideas to fulfill the prompt
  • "Tweetstorming" - Social platform for ideation. Chainstorming via twitter. Custom website that allows users to see other users’ questions, reply to them selectively, browse other users’ replies to prompts, and vote on their favorite ideas to select them
  • Can be used individually or combined.
  • Significance: Ideation through redistribute existing ideas in different contexts to "unconventionalize" it.

Yang, M. C. (2009). Observations on concept generation and sketching in engineering design.

Yang, M. C. (2009). Observations on concept generation and sketching in engineering design. Research in Engineering Design, 20(1), 1-11.

https://pdfs.semanticscholar.org/dc8f/c7d181f4994dc7044ecb3e9e9454b765886f.pdf

Takeaways

  • Hypothesis 1: The quantity of concepts generated at the beginning of a design project correlates with design outcome.
  • Hypothesis 2: The quantity of sketches generated during a project correlates with its design outcome.
  • Outcome: Quantified by final grade and contest results.
  • Conclusion 1: Concept generation measured in the form of morphology charts showed a statistically significant correlation with both project and final term grade in the introductory course.
  • Conclusion 2: Morphology charts in the advanced courses did not show a statistically significant correlation.
  • Conclusion 3: Designer can sketch very little overall and achieve a better design grade, as long as the bulk of dimensioned drawings (and perhaps prototyping) are cre- ated early on in the design process

Rogers, Y., Sharp, H., & Preece, J. (2011). Chapter 6: The Process of Interaction Design

Rogers, Y., Sharp, H., & Preece, J. (2011). Chapter 6: The Process of Interaction Design. In Interaction Design: Beyond Human-Computer Interaction. John Wiley & Sons.

http://www.wiley.com/legacy/wileychi/interactiondesign/pdf/ID_ch6.pdf

Takeaways:

  • The interaction design process consists of four basic activities: identifying needs and establishing requirements, developing alternative designs that meet those requirements, building interactive versions of the designs so that they can be communicated and assessed, and evaluating them.
  • Key characteristics of the interaction design process are explicit incorporation of user involvement, iteration, and specific usability criteria.
  • Before you can begin to establish requirements, you must understand who the users are and what their goals are in using the device.
  • Looking at others’ designs provides useful inspiration and encourages designers to consider alternative design solutions, which is key to effective design.
  • Usability criteria, technical feasibility, and users’ feedback on prototypes can all be used to choose among alternatives.
  • Prototyping is a useful technique for facilitating user feedback on designs at all stages.
  • Lifecycle models show how development activities relate to one another.
  • The interaction design process is complementary to lifecycle models from other fields.

Software engineering lifecycle models:

  1. Waterfall lifecycle - linear model
  2. Spiral lifecycle - iterative framework, uses risk analysis and prototyping.
  3. Rapid Applications Development (RAD)
    • 6 month time box for system delivery
    • JAD (Joint Application Developemnt) workshops, users/devs collaborate to flesh out requirements of the system.
  4. Star Lifecycle Model
    • No ordering of activities, all interconnected.
    • Must always evaluate at end of each activity.
  5. Usability Engineering Lifecycle
    • three tasks: requirements analysis, design/ testing/development, and installation
    • uses a "style guide" as mechanism for capturing/disseminating usability goals of the project.

UAE Diagram

In [ ]:

Week 5 - Mental Models and Representations

MacKenzie, I.S. (2013). Section 3.4: Mental Models & Metaphor

MacKenzie, I.S. (2013). Section 3.4: Mental Models & Metaphor. Human-Computer Interaction: An Empirical Research Perspective. (pp. 88-92). Waltham, MA: Elsevier. https://gatech.instructure.com/courses/340124/files/folder/Required%20Readings

Takeaways:

  • Implementation models - Bad. "impose on the user a set of interactions that follow the inner workings of an application", doesn't follow user's conceptual model
    • Example: "software-based fax product where the user is paced through a series of agonizing details and dialogs"

Good mental models:

  1. Icons - not all are immediately understood. Can add balloons as signifiers.
  2. Compass / clock face as metaphor for direction
    • Example: Using clock metaphor to navigate the blind.

MacKenzie, I.S. (2013). Section 3.8: Interaction errors

MacKenzie, I.S. (2013). Section 3.8: Interaction errors. Human-Computer Interaction: An Empirical Research Perspective. (pp. 111-116). Waltham, MA: Elsevier. https://gatech.instructure.com/courses/340124/files/folder/Required%20Readings

4 Examples of Interaction Errors

  1. Losing info while working on their computers
  2. Password prompt - Is my caps lock on or off? What I type is hidden, so can't verify.
  3. Selecting text and dragging - scrolling down to edge of screen changes velocity-control.
  4. Inconsistencies in focus advancement

Takeaways:

  • "The absence of expectations keeps the user on guard"
  • "Where the consequences of errors are small, such as an extra button click or a gaze shift, errors tend to linger"
  • "User experiences exist as collections of microstrategies. Whether booking a vacation online or just hanging out with friends on a social networking site, big actions are collections of little actions. To the extent possible, user actions form the experience, our experience. It is unfortunate that they often exist simply to serve the needs of the computer or applicatio"
  • "Another reason little errors tend to linger is that they are often deemed user errors, not design, programming, or system errors"

Norman, D. (2013). Chapter 5: Human Error? No, Bad Design

Norman, D. (2013). Chapter 5: Human Error? No, Bad Design. In The Design of Everyday Things: Revised and Expanded Edition. (pp. 162-216). Arizona: Basic Books. https://gatech.instructure.com/courses/340124/files/folder/Required%20Readings

Takeaways:

  • Most common errors, "nature of the tasks and procedures that require people to behave in unnatural ways"
    • Others: Time stress, deliberate risk taking
  • Blaming the individual doesn't solve systemic errors.
  • Root cause analysis: investigate accident until the single, underlying cause is found.
  • Five Whys: originally developed by Sakichi Toyoda and used by the Toyota Motor Company
    • Ask the why five times, keep asking until you uncovered the true underlying causes.
    • Doesn't guarantee success in finding root cause(s)
  • "Why do people err? Because the designs focus upon the requirements of the system and the machines, and not upon the requirements of people."
  • Deliberate risk taking - Types
    1. Frequent noncompliance
    2. Inappropriate rules or procedures that invite violation, and culture that rewards it.
  • Slips and errors (covered in lecture notes)
  • Types of action slips:
    1. Capture slips: situation where, instead of the desired activity, a more frequently or recently performed one gets done instead
    2. Description-similarity slips: Error by acting upon an item similar to the target.
    3. Mode-error slips: Error when a device has different states in which the same controls have different meanings.
  • Social pressures lead to mistakes. How to solve? Need to reward safety and put it above economic pressures.
  • Checklists - collaboratively followed checklists are effective.
    • Bad to impose a sequential structure to task execution (unless the task itself requires it).
  • Toyota's Jidoka philosophy - "automation with a human touch"
    • If a worker notices something wrong, the worker is supposed to report it, sometimes even stopping the entire assembly line if a faulty part is about to proceed to the next station.
    • Punishment is applied to the group, motivates reporting.
  • "Poka-yoke", Shigeo Shingo - constraints to avoid error.
    • e.g. add simple fixtures, jigs, or devices to constrain the operations so that they are correct
  • Designing for error:
    • Understand the causes of error and design to minimize those causes.
    • Dosensibilitychecks.Doestheactionpassthe“commonsense”test?
    • Make it possible to reverse actions—to “undo” them—or make it harder to do what cannot be reversed.
    • Make it easier for people to discover the errors that do occur, and make them easier to correct.
    • Don’t treat the action as an error; rather, try to help the person complete the action properly. Think of the action as an approximation to what is desired.
  • Design lessons:
    1. Add constraints to block errors.
    2. Allow "undo"
    3. Liberal use of confirmation and error messages
    4. Implement "sensibility checks" - capture outlier actions, they're probably a mistake/slip.
  • Swiss cheese model of errors - many small errors lead to a catastrophe. Prevent by:
    • Adding more slices of cheese
    • Reduce the number of holes, or make them smaller
    • Alert the human operators when holes have lined up.

Design Principles for Dealing with Error

  1. Put the knowledge required to operate the technology in the world.
    • Don't keep it in your head. Share it and have collective responsibility.
  2. Use the power of natural and artificial constraints: physical, logical, semantic, and cultural.
  3. Bridge the two gulfs, the Gulf of Execution and the Gulf of Evaluation.
    • On the execution side, provide feedforward information: make the options readily available.
    • On the evaluation side, provide feedback: make the results of each action apparent.

Mander, R., Salomon, G., & Wong, Y. Y. (1992, June). A “pile” metaphor for supporting casual organization of information

Mander, R., Salomon, G., & Wong, Y. Y. (1992, June). A “pile” metaphor for supporting casual organization of information. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 627-634). ACM. http://www.cs.columbia.edu/~feiner/courses/csw4170/resources/p627-mander.pdf

Takeaway:

  • The authors present a prototype system called PILE, which allows users to create virtual piles of digital documents or files. These piles function as informal groupings that can be easily created, modified, and navigated. The system incorporates visual representations of the piles, providing a graphical interface that mimics physical piles of papers.
  • Overall, the paper presents the concept of using a pile metaphor as an alternative approach to organizing digital information, emphasizing its flexibility and ease of use for casual organization purposes. The PILE system demonstrates the viability of this approach and highlights the potential benefits it offers for users in managing their digital information.

Results:

  • Although each user had a clear preference for one of our methods of pile creation (“pile-centered” or “document-centered”), neither method was judged to be clearly superior.
  • In the “document-centered” model, users liked the ability to grab an individual document within a pile.
    • A problem with this model was that users were not sure how to move a pile as a unit, since selecting any part of the pile led to moving an individual item rather than the pile as a whole.
  • In the “pile-centered” model, users liked the way the system automatically aligned the items in the pile, the ability to move a pile as a unit, and the highlight- ing that indicated a pile was ready to accept an item.
    • A problem with this model was the difficulty of selecting an individual item within the pile,

END OF MATERIAL FOR TEST 1

Week 6 - Prototyping

Houde, S., & Hill, C. (1997). What do prototypes prototype?

Houde, S., & Hill, C. (1997). What do prototypes prototype? In M. Helandar, T.K. Landaeur, & P. Prabhu (Eds). Handbook of Human-Computer Interaction, 2. (pp. 367-381). Elsevier Science. http://www.itu.dk/people/malmborg/Interaktionsdesign/Kompendie/Houde-Hill-1997.pdf

"The goal of this chapter is to establish a model that describes any prototype in terms of the artifact being designed, rather than the prototype's incidental attrib- utes."

"By focusing on the purpose of the prototype--that is, on what it prototypes--we can make better decisions about the kinds of prototypes to build."

Goals of prototyping new functionalities:

  1. If the function is well understood, but the goal is to present it in a new way - Focus on prototyping how the new artifact looks and feels.
  2. If the artifact's function is based on a new technique - Focus on prototyping how to implement the design.

Problems of Prototypes

  • Organization may have narrow view of what a prototype should be.
  • Focusing too much on the attributes of the prototype itself (ie. which tool was used to create it, fidelity).
    • Resolution: Amount of detail
    • Fidelity: Closeness to the eventual design.

Prototype of a Model

What prototypes prototype:

  1. Role - The way in which the feature is useful to users
  2. Implementation - Technique of using the feature, "nuts and bolts"
  3. Look and feel - Sensory experience of using the feature

Integration prototypes - represent the "complete user experience" of an artifact.

Takeaways

  1. Define "prototype" broadly - simple can be effective
  2. Build multiple prototypes - make a lot of simple prototypes, be prepared to toss them.
  3. Know your audience - Choose how polished you want your prototype to be depending on your audience.
  4. Know your prototype - Be clear about what design questions are being explored with a given prototype.

Beaudouin-Lafon, M., & Mackay, W. (2003). Prototyping tools and techniques. Human Computer Interaction-Development Process. (pp. 101-142).

Beaudouin-Lafon, M., & Mackay, W. (2003). Prototyping tools and techniques. Human Computer Interaction-Development Process. (pp. 101-142). https://www.lri.fr/~mackay/pdffiles/Prototype.chapter.pdf

Takeaways

  • More focused on interactive system prototypes, as opposed to other fields (architectural, etc).
  • What prototypes do:
    1. Support creativity by generating ideas
    2. Encourage communication
    3. Encourage early evaluation.
  • How prototypes are analyzed
    1. Representation - forms of the prototype
    2. Precision - level of detail (resolution)
    3. Interactivity - extent to which the user can actually interact
    4. Evolution - expected life-cycle of the prototype.
  • Offline prototyping is valueable even in software
    • fast to iterate
    • allows more creativity, less constraints
    • can be created by non-programmers (important)
  • Design space: constrains design possibilities along some dimensions, while leaving others open for creative exploration.
    • Expand: brainstorming, video brainstorming
    • Contract: Selecting alternatives
  • Prototype strategies:
    1. Horizontal - breadth
    2. Vertical - depth
    3. Task-oriented - Fulfill a task start to finish
    4. Scenario - Less focus on individual, independent tasks but follow a realistic scenario of how it would be used in a real-world setting.
  • Goes into details of UI design, particularly web design topics

Fender, A. R. & Holz, C. (2022). Causality-preserving Asynchronous Reality.

Fender, A. R. & Holz, C. (2022). Causality-preserving Asynchronous Reality. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://programs.sigchi.org/chi/2022/index/content/68789

Takeaways:

  • Augmented reality - problem is bridging between virtual world and reality.
  • Solution: Create a system where someone in virtual world can play back events (ie. "causality graph of co-dependent events"). Gives context to what was going on when they were in virtual world.
  • How it works:
    1. Focus mode: User blocks outside world.
    2. Someone else might interrupt, or leave something in their room.
    3. User, approaching an object left, sees a playback of the event.
  • Motivation: Make synchronous daily activities able to be retrieve asynchronously.
    • Asynchronous communication is still completely explicit, ie. sender must consciously decide to send an email, voice recording, etc.
    • But has advantage. that receive may choose when to process the message. This paper tries to do this in real-life events.
  • Contributions:
    1. Asynchronous Reality as a concept.
    2. System called AsyncReality as an instance of said concept.
  • Implementation challenge: "Not the playback itself, but how and when to trigger event playback and in which order to play back the events"

Kim, J., Choi, Y., Xia, M., & Kim, J. (2022). Mobile-Friendly Content Design for MOOCs: Challenges, Requirements, and Design Opportunities.

Kim, J., Choi, Y., Xia, M., & Kim, J. (2022). Mobile-Friendly Content Design for MOOCs: Challenges, Requirements, and Design Opportunities. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://programs.sigchi.org/chi/2022/index/content/68914

Abstract:

Most video-based learning content is designed for desktops without considering mobile environments.

We (1) investigate the gap between mobile learners’ challenges and video engineers’ considerations using mixed methods and (2) provide design guidelines for creating mobile-friendly MOOC videos.

To uncover learners’ challenges, we conducted a survey (n=134) and interviews (n=21), and evaluated the mobile adequacy of current MOOCs by analyzing 41,722 video frames from 101 video lectures.

Interview results revealed low readability and situationally-induced impairments as major challenges. The content analysis showed a low guideline compliance rate for key design factors.

We then interviewed 11 video production engineers to investigate design factors they mainly consider. The engineers mainly focus on the size and amount of content while lacking consideration for color, complex images, and situationally-induced impairments.

Finally, we present and validate guidelines for designing mobile-friendly MOOCs, such as providing adaptive and customizable visual design and context-aware accessibility support.

In [ ]:

Week 7 - Context and Distributed Cognition

Week 8 - Experiments and Evaluation

Week 9 - Artifacts, Interfaces, and Politics

Week 10 - Evaluation and Agile Development